Flow systems, such as industrial systems, can be described and modelled in several ways, and the models obtained are used for many different tasks, such as supervision, control, measurement validation, alarm analysis, fault diagnosis and sensor fault detection.
Flow systems can for example be modelled by a model comprising qualitative descriptions of the system or by a so-called rule-based model, i.e., a model modelling the system using rules or logical statements describing how different components of the system interact with each other.
Today more sophisticated models for modelling industrial system are available, for example so-called causal models. A causal model is a model, which models parts of the system and the causal relationship between different parts, e.g. components, of the system, i.e., how the parts affect each other.
One example of a causal model is the so-called functional model. Functional models are used to model flow systems by identifying the overall goal the system must achieve, the functions the system must perform to fulfil the goal, and the behaviour of the physical structure in order to realise the functions. For example, the modelled system can be an electronic device, a process control system in a factory, customer-service activities in a bank, a robot, or the entire Internet.
The strength of functional modelling is in its ability to cope with the complexity in large flow systems. This is due to the fact that the overall goal and functions in complex flow systems are many and often very hard to recognise using classical modelling methods. Thus, functional modelling has been used to describe functions of human-machine systems, to perform diagnosis and planning in industrial plants, and to identify failures and their consequences in such plants.
One example of a functional model is the multilevel flow model (MFM). The basic idea of MFM is to model a man-made system designed and used with certain purposes in mind. The main strength of a multilevel flow model is that it is easy to build a model of a target system using MFM. Thus, MFM is preferably used in the modelling of large flow systems.
Multilevel flow models (MFM) are models using a graphical language representing the goals and functions of complex systems. The main strength of MFM is the ability to describe very complex systems using a small number of modelling element, also called functions. For each of the legal connections between the MFM functions, a set of causal rules is defined. These causal rules describe how qualitative states of the functions affect each other. The causal rules may be used with one or more diagnostic method, such as, alarm analysis, discrete sensor validation, or failure mode analysis.
In a flow system it may not always be true that two connected components affect the operating condition of each other. Thus, this must also be implemented in the model of the flow system, i.e., how two connected functions affect the state of each other.
In MFM it is assumed that all connected functions, modelling parts of the flow system, affect each other in both directions. For example it is obvious that a pump providing water to a closed tank affects the water level in the tank and that the level in the tank also affects the flow through the pump. If the level in the tank is too high, the pump will not be able to transport more water to the tank since the tank is full, thus the flow through the tank will be too low. However, if the tank is open instead of closed the level in the tank will not affect the flow through the pump, but the water will just flow over the edges of the tank rather than blocking the flow through the pump. A drawback with MFM of today is that it can not handle this latter case, since it is assumed in MFM that all connected components or parts of the flow system affect each other. As illustrated above, this may not always be the case.
Another drawback with MFM is that it can not handle the case when the casual relationship between connected components for example changes over time. For example, MFM can not model the case when the closed tank described above has a removable lid, which is removed during the operation of the flow system. Thus MFM can not model the case when a flow system in a first operation state has a lid and in a second operation state has not a lid.
One solution to the above-mentioned problem would be to provide more MFM symbols or model elements to represent various types of objects such as open tanks, closed tanks, centrifugal pumps, etc. However, this solution quickly becomes unmanageable, since it may be difficult to find the appropriate symbol to use in a specific system, and the modelling effort quickly becomes difficult.
In the Ph.D. thesis “Knowledge based support for situation assessment in human supervisory control” by Johannes Petersen, DTU Lyngby, Denmark, 2000, 00-A-897, ISBN 87-87950-84-7, an MFM model for handling some special cases of causality is disclosed. However, Petersen does not provide a general solution of the problem and thus the causality between all functions, used to model the flow system, is not possible. It is for example in the system disclosed by Petersen not possible to handle causality of a transport function or a barrier function, and Petersen does not propose a solution to the problem.
Further, with the disclosed system it is not possible to handle the causality dynamically, i.e. it is not possible to handle the case when the influence two connected functions affects each other with changes over time or due to other parameters controlling the operation states of the components of the flow system.
The system disclosed by Petersen is thus a static system and does not provide a general solution of how to model dynamical causalities of a flow system.
Further, no one known to the inventors of the present invention provides a solution to the problem of modelling, in a general and dynamical way, the causality between connected model elements modelling a flow system without increasing the modelling complexity.